1. Technical Field
This application generally relates to the field of object classification and, in particular, to the use of synthetic data in the classification of pedestrian pose.
2. Background Information
A vehicle (e.g., an automobile) outfitted with a pedestrian detection system can warn its driver that a pedestrian is nearby. However, pedestrian detection alone is not sufficient. The danger of the situation should be assessed also. Only when there is the risk of an accident should a warning be produced. Otherwise, the driver will be unnecessarily distracted. The danger of the situation is related to, for example, whether the pedestrian is likely to step in the path of the vehicle.
“Object classification” refers to the task of automatically classifying an object in a video image or a still image. For example, a classification system may determine whether a person (e.g., a pedestrian) in a still image is facing left, facing right, facing front, or facing back. Pedestrian pose classification may be used, for example, in a vehicle to increase the safety of the driver of the vehicle, pedestrians, bicyclists, and any other person sharing the road with the vehicle.
Many problems exist with current object classification systems. One problem is the lack of an extensive training set for training the object classification model. A training set, which includes positive samples (images including an object of a particular class) and negative samples (images not including an object of the particular class, such as images including an object of another class), is provided to a machine learning algorithm to produce an object classification model.
Furthermore, when generating a new training set for a certain type of object, each image is manually annotated with certain pieces of information. For example, the classification of the object present in the image and/or certain parameters of the object present in the image (e.g., color of the object and location of the object within the image) may be added to the image. The machine learning algorithm uses those annotations and images to generate a model for classifying the object. The annotation process can be tedious and time consuming.